Exploring causal relationships for qualitative data analysis in HCI and social science research enables the understanding of user needs and theory building. However, current computational tools primarily characterize and categorize qualitative data; the few systems that analyze causal relationships either inadequately consider context, lack credibility, or produce overly complex outputs. We first conducted a formative study with 15 participants interested in using computational tools for exploring causal relationships in qualitative data to understand their needs and derive design guidelines. Based on these findings, we designed and implemented QualCausal, a system that extracts and illustrates causal relationships through interactive causal network construction and multi-view visualization. A feedback study (n=15) revealed that participants valued our system for reducing the analytical burden and providing cognitive scaffolding, yet navigated how such systems fit within their established research paradigms, practices, and habits. We discuss broader implications for designing computational tools that support qualitative data analysis.
Given the rapid development of AI technologies, it is crucial to proactively mitigate their potential ethical harms. In this work, we apply interdisciplinary futuring tactics such as speculative fiction to elucidate potentially unforeseen consequences of future AI technologies. While speculative fiction has been applied in HCI as a participatory design method, our research advocates for advancing beyond a strictly participatory role for writers and to instead include them as expert collaborators. We engage in collaborative autoethnography (CAE) as creative writers with STEM backgrounds to reflect on experiences in our AI ethics community-based speculative fiction workshop series and on our perspectives on collaboration between technologists and writers. This paper contributes: (1) our method for community-based speculative fiction including writer workshops, a website, and print anthology, (2) our argument for why writers should be expert collaborators in HCI and (3) our guidelines for interdisciplinary collaboration between writers and technologists for proactive AI ethics.
Reflexive Thematic Analysis is an increasingly popular method of qualitative analysis in many disciplines, including HCI. However, previous work has questioned the quality of its application, often finding it misunderstood and misapplied. To establish a snapshot of current practice in HCI, we performed a scoping review to examine 147 CHI papers from a single year that purported to use the method. Markers of good and poor practice were assessed based on writings and guidelines by the method's originators. Similar to reviews in other domains, we found widespread issues such as methodological incongruence, insufficient detail, and poorly conceptualised and reported themes. Despite this, we highlight encouraging pockets of good practice. We conclude that HCI researchers should engage more holistically with Reflexive TA, and question whether it is always the most appropriate choice of method. To this end we offer several recommendations for improvement.
Fieldwork provides essential access to participants’ everyday practices, but this close engagement also exposes researchers to unpredictable ethical dilemmas. Navigating these dilemmas can be challenging for researchers because ethics principles, codes of conduct, or assessment by ethics boards do not address all fieldwork challenges that researchers might encounter. Addressing ethical dilemmas inevitably involves researchers’ own values and judgements, yet, little is known about what and how researchers’ values inform their decisions in practice. This study investigates researchers’ value enactment, drawing on four workshops with 21 participants who conduct user research fieldwork. We identified (1) a collection of values that orient researchers’ practice, extending current understandings of what values operate in design research; and (2) a set of seven questions, each anchored in a declared value that surface recurring tensions in fieldwork. Together, these insights deepen the understanding of ethical decision-making in fieldwork, supporting ethical reflection and value alignment within teams.
Location-Based Services (LBS) such as ride-sharing, accommodation, food delivery, and location-driven social media platforms entangle digital systems with physical spaces, thereby generating impacts that extend beyond users to others who share the same environments. Existing design approaches struggle to address the dual challenge of value tensions that arise in shared physical spaces and the locality-specific contexts in which LBS operate. To respond, we introduce Location-Aware Value Sensitive Design (LA-VSD), a domain-specific adaptation of VSD tailored to the distinctive characteristics of LBS. LA-VSD guides designers through three heuristics to help (1) identify and prioritise stakeholders through local space-sharing scenarios, (2) adapt empirical methods to capture values and tensions in context, and (3) support value-aligned interactions across both digital and physical layers of the service. Through a case study of e-scooter sharing in Melbourne, Australia, we demonstrate how LA-VSD enables more grounded, context-aware, and actionable design of LBS.
In Human-Computer Interaction, eliciting user attitudes and beliefs is crucial for understanding user interactions with technology. Existing elicitation methods range from expressive open-ended text to structured formats like Likert scales. Expressive methods yield rich insights but are difficult to systematically analyze. On the other hand, structured methods guide users to efficiently map attitudes and beliefs to clear visual scales, yet may oversimplify complex attitudes and beliefs. Recent work has explored alternative methods including visual elicitation techniques; however, the understanding of how users mentally represent attitudes and beliefs remains limited, making it challenging to validate the effectiveness of these techniques. Through a qualitative study of US-based participants (N=41), we captured how people mentally represent their attitudes and beliefs through free-form drawings and complementary textual descriptions. Our findings reveal how the strategies participants employed to represent attitudes and beliefs can inform the design of future visual elicitation techniques that balance both expressiveness and analyzability.
Reflexive Thematic Analysis (RTA) is a critical method for generating deep interpretive insights. Yet its core tenets, including researcher reflexivity, tangible analytical evolution, and productive disagreement, are often poorly supported by software tools that prioritize speed and consensus over interpretive depth. To address this gap, we introduce Reflexis, a collaborative workspace that centers these practices. It supports reflexivity by integrating in-situ reflection prompts, makes code evolution transparent and tangible, and scaffolds collaborative interpretation by turning differences into productive, positionality-aware dialogue. Results from our paired-analyst study (N=12) indicate that Reflexis encouraged participants toward more granular reflection and reframed disagreements as productive conversations. The evaluation also surfaced key design tensions, including a desire for higher-level, networked memos and more user control over the timing of proactive alerts. Reflexis contributes a design framework for tools that prioritize rigor and transparency to support deep, collaborative interpretation in an age of automation.